Method for predicting a level of QoE of an application intended to be run on a wireless user equipment

ABSTRACT

The present disclosure is directed toward a device and a method for evaluating a wireless link established between an access point and a user equipment. The device and method include determining a level of Quality of Experience of an application intended to be run on the user equipment using a mapping between a parameter representative of the QoE of the application under different wireless transmission conditions and sets of parameters representative of said different transmission conditions of the wireless link.

REFERENCE TO RELATED EUROPEAN APPLICATION

This application claims priority from European Patent No. 16305390.3,entitled “METHOD FOR PREDICTING A LEVEL OF QOE OF AN APPLICATIONINTENDED TO BE RUN ON A WIRELESS USER EQUIPMENT,” filed on Apr. 1, 2016,the contents of which are hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The invention relates to the field of wireless nodes and respectivedevices communicating with each other via a wireless communication.

BACKGROUND

Access gateways are widely used to connect devices at the home to theInternet or any other wide area network (WAN). Access gateways use inparticular Digital Subscriber Line (DSL) technology that enables a highdata rate transmission over copper lines or optical lines. Residentialgateways, but also other devices such as routers, switches, telephonesand set-top boxes, are understood in this context as Customer PremisesEquipment (CPE) devices.

Access gateways including wireless technology have a key role in today'shome and professional environments. A mechanism for connecting wirelessdevices to a Local Area Network (LAN) is called Wi-Fi, which is a brandname of the Wi-Fi Alliance for devices using the IEEE 802.11 family ofstandards for wireless data transmission. The IEEE 802.11 standardsdefine two types of wireless nodes, a general wireless device that canconnect to other devices called a station (denoted as STA) and a specialtype of a STA that is in control of the network, namely an Access Point(denoted AP). A Wi-Fi network, often called a WLAN (Wireless Local AreaNetwork), consists of an AP with one or several STA connected to the AP.

Due to its flexible and “invisible” nature, a lot of LAN equipments areutilizing Wi-Fi rather than the classical wired Ethernet approach. Thiswidespread usage of wireless LAN has exposed however a serious downsideusing a shared medium technology: interference. Interference, both Wi-Fiand non-Wi-Fi related, leads to a degraded user experience due to thenature of IEEE 802.11. In its most common form, IEEE 802.11 networksapply a medium access method in which collisions are avoided by sensingthat the medium is used (denoted as CSMA-CA for Carrier Sense MultipleAccess-Collision Avoidance). This uses a technique referred to as “ClearChannel Assessment” (CCA). Clear channel assessment determines whether awireless communication channel is “occupied”, e.g., “busy” with anotherwireless communication and/or has an amount of interference that makesthe wireless communication channel unsuitable for communication. In thisway, it is determined whether the wireless communication channel isavailable or not available for communication, e.g. occupied or notoccupied. The medium access method is also commonly known as “listenbefore talk”, describing the essence of the method. Interference fromany nature can hence block the medium and force all nodes to remainsilent for a certain amount of time.

Another impact of interference can be packet loss at the receiver side,leading to a reduction of the physical data rate. In this case, theinterference is not detected by the CCA of the transmitter, but isdecreasing the SINR (Signal to Noise and Interference Ratio) of theWi-Fi packets as seen by the receiver.

Therefore, in certain circumstances, the Wi-Fi connection can sufferfrom poor performance and even connection loss. Some of thesecircumstances are obvious and easy to explain to an end user. Forexample, if the distance between the station and the access point is toolarge, then signal levels are low and performance will degrade. Othercircumstances are “invisible” and not understood by the end user, e.g. ahidden node. A hidden node is invisible to some of the nodes of anetwork, leading to a practical failure of the CSMA-CA method, which cancause packet collision/corruption over air. In many cases, the end useris not able to diagnose the problem source and correct the issue.

With the recent development of tablets, laptops and smartphones there isan increase in the use of Wi-Fi. As a consequence, in-home Wi-Fi networkconnectivity becomes one of the main Internet service provider supportcosts and causes for help-desk calls. Indeed, as Wi-Fi connections arevulnerable to performance problems due to the shared medium, an end usermay observe a decrease in Quality of Experience (QoE) of theapplications he/she is currently running on one of his/her wirelessequipments, such as an increase in the loading time of a website. Theend user may mistakenly assume there is an issue with the serviceoffered by the Internet Service Providers (ISP).

Internet service providers are therefore searching for ways to get abetter understanding of the end user's wireless environment includinglink quality and performance and its impact on the QoE of the end user.

The present invention has been devised with the foregoing in mind.

SUMMARY OF INVENTION

According to a first aspect of the invention there is provided acomputer implemented method for predicting a level of Quality ofExperience (QoE) of an application intended to be run on a userequipment, said method comprising:

-   -   computing said level of QoE of said application using a set of        parameters representative of transmission conditions of a        wireless link established between said user equipment and an        access point, said set of parameters representative of        transmission conditions of the wireless link being collected by        said access point being prior the transmission of data between        said user equipment and said access point.

Such a method enables to accurately predict an expected Quality ofExperience (QoE) for an application intended to be run on a userequipment, such as a smartphone, connected to the Internet through awireless link established between said user equipment and an accesspoint, such as a residential gateway, said wireless link being forexample a Wi-Fi link. Thus, such a method enables to determine thosecases where the end user expectations in terms of QoE are not metbecause of the conditions of the wireless link.

This is made possible thanks to the knowledge of parametersrepresentative of the transmission conditions of the wireless link—i.e.CCA statistics and transmission scheme chosen by the transmitter.

The method further comprises, prior to computing the level of QoE:

-   -   measuring at least one parameter representative of the QoE of        the application under different wireless transmission        conditions, said different wireless transmission conditions        being defined by different sets of parameters representative of        transmission conditions of the wireless link, called learning        sets,    -   computing a mapping between the different learning sets and the        parameter representative of the level of QoE of the application.

Prior to predicting the expected level of QoE of an application intendedto be run on a user device, a learning phase is executed. During thelearning phase, a parameter representative of the QoE of the applicationis measured several times under different wireless transmissionconditions in order to establish a correlation between the parametersrepresentative of a transmission conditions of the wireless linkdefining the different transmission conditions of the wireless link andthe parameter representative of the QoE of the application. This isdone, for example, by introducing attenuation on the transmission pathbetween the access point and the user equipment or interferences due todifferent types of wireless communications, e.g. Wi-Fi or non Wi-Ficommunications.

The method further comprises:

-   -   determining the parameters of the learning sets which are        significant influence on the level of QoE of said application.

Using all the parameters of the learning set does not improve theaccuracy of the prediction of the level of QoE to be expected, it isthus useless to use all the parameters of the learning set.

According to another aspect of the method, the application intended tobe run on the user equipment is a web-browsing application.

Web-browsing is responsible for a large fraction of the Internet trafficin local area networks and is consequently the application for which endusers tend to be demanding in terms of QoE. It is therefore interestingto predict a level of QoE to be expected for web-browsing.

According to another aspect of the method, the parameter representativeof the QoE of the application under different wireless transmissionconditions is the page load time.

The page load time is considered one of the main indicators when itcomes to web-browsing experience.

The method further comprises:

-   -   computing a mapping between the page load time and a mean        opinion score.

Mapping the page load time and the mean opinion score avoids costly userinvolvement.

According to another aspect of the method, the parameters representativeof the transmission conditions of the wireless link of significantinfluence on the level of QoE of said application are an averagephysical layer transmission rate, a frame delivery ratio, and parametersrepresentative of a business of a transmission medium of the wirelesslink due to interferences from wireless equipments located in a vicinityof the user equipment.

During the learning phase, it is determined that among all theparameters of the learning sets these four parameters are the morerelevant for an accurate prediction of the level of QoE. Theinterferences from other wireless equipments are due to different typesof wireless communications, e.g. Wi-Fi or non Wi-Fi communications.

According to another aspect of the method, the parameters representativeof the transmission conditions of the wireless link are obtained bymonitoring data transmission conditions through the wireless link duringnormal usage of the WLAN.

According to another aspect of the method, the parameters representativeof the transmission conditions of the wireless link are collected by theaccess point.

Since the access point is the master node of every wireless local areanetwork, it is the best location to monitor and collect data related towireless transmission.

According to another aspect of the method, computing the level of QoE ofsaid application is based on the mapping of the parametersrepresentative of the transmission conditions of the wireless link ofsignificant influence on the level of QoE and the mean opinion score.

The method relies on the use of the correlation established between theparameters representative of the transmission conditions of the wirelesslink of significant influence on the level of QoE and the mean opinionscore during the learning phase.

According to another aspect of the method, computing the level of QoE ofsaid application is based on the mapping of the parametersrepresentative of the transmission conditions of the wireless link andthe mean opinion score.

Another aspect of the invention is a circuit comprising a processor, amemory and a wireless node, the memory comprising instructions, which,when performed by the processor, perform a method according to anembodiment of the invention.

Another aspect of the invention is a gateway comprising a circuitcomprising a processor, a memory and a wireless node, the memorycomprising instructions, which, when performed by the processor, performa method according to an embodiment of the invention. Some processesimplemented by elements of the invention may be computer implemented.Accordingly, such elements may take the form of an entirely hardwareembodiment, an entirely software embodiment (including firmware,resident software, micro-code, etc.) or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit”, “module” or “system’. Furthermore, such elements may take theform of a computer program product embodied in any tangible medium ofexpression having computer usable program code embodied in the medium.

Since elements of the present invention can be implemented in software,the present invention can be embodied as computer readable code forprovision to a programmable apparatus on any suitable carrier medium. Atangible carrier medium may comprise a storage medium such as a floppydisk, a CD-ROM, a hard disk drive, a magnetic tape device or a solidstate memory device and the like. A transient carrier medium may includea signal such as an electrical signal, an electronic signal, an opticalsignal, an acoustic signal, a magnetic signal or an electromagneticsignal, e.g. a microwave or RF signal.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of exampleonly, and with reference to the following drawings in which:

FIG. 1 represents an access point communicating with a station via awireless communication,

FIG. 2 represents data rates of a wireless communication according toFIG. 1, and

FIG. 3 represents a flow chart illustrating the steps of a method forpredicting a level of QoE of an application intended to be run on astation according to one or more embodiments of the invention.

DETAILED DESCRIPTION

It should be understood that the elements shown in FIG. 1 may beimplemented in various forms of hardware, software or combinationsthereof. Preferably, these elements are implemented in a combination ofhardware and software on one or more appropriately programmedgeneral-purpose devices, which may include a processor, memory andinput/output interfaces. Herein, the phrase “coupled” is defined to meandirectly connected to or indirectly connected with through one or moreintermediate components. Such intermediate components may include bothhardware and software based components.

The present description illustrates the principles of the presentdisclosure. It will thus be appreciated that those skilled in the artwill be able to devise various arrangements that, although notexplicitly described or shown herein, embody the principles of thedisclosure and are included within its spirit and scope.

All examples and conditional language recited herein are intended forinstructional purposes to aid the reader in understanding the principlesof the disclosure and the concepts contributed by the inventor tofurthering the art, and are to be construed as being without limitationto such specifically recited examples and conditions.

Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosure, as well as specific examples thereof, areintended to encompass both structural and functional equivalentsthereof. Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture, i.e., any elements developed that perform the same function,regardless of structure.

Thus, for example, it will be appreciated by those skilled in the artthat the block diagrams presented herein represent conceptual views ofillustrative circuitry embodying the principles of the disclosure.Similarly, it will be appreciated that any flow charts, flow diagrams,state transition diagrams, pseudocode, and the like represent variousprocesses which may be substantially represented in computer readablemedia and so executed by a computer or processor, whether or not suchcomputer or processor is explicitly shown.

The functions of the various elements shown in the figures may beprovided through the use of dedicated hardware as well as hardwarecapable of executing software in association with appropriate software.When provided by a processor, the functions may be provided by a singlededicated processor, by a single shared processor, or by a plurality ofindividual processors, some of which may be shared. Moreover, explicituse of the term “processor” or “controller” should not be construed torefer exclusively to hardware capable of executing software, and mayimplicitly include, without limitation, digital signal processor (“DSP”)hardware, read only memory (“ROM”) for storing software, random accessmemory (“RAM”), and nonvolatile storage.

Other hardware, conventional and/or custom, may also be included.Similarly, any switches shown in the figures are conceptual only. Theirfunction may be carried out through the operation of program logic,through dedicated logic, through the interaction of program control anddedicated logic, or even manually, the particular technique beingselectable by the implementer as more specifically understood from thecontext.

In the claims hereof, any element expressed as a means for performing aspecified function is intended to encompass any way of performing thatfunction including, for example, a) a combination of circuit elementsthat performs that function or b) software in any form, including,therefore, firmware, microcode or the like, combined with appropriatecircuitry for executing that software to perform the function. Thedisclosure as defined by such claims resides in the fact that thefunctionalities provided by the various recited means are combined andbrought together in the manner which the claims call for. It is thusregarded that any means that can provide those functionalities areequivalent to those shown herein.

In the following description, example methods for predicting a level ofQoE of an application intended to be run on a station or user equipment,are described, as well as a device performing the methods. For purposesof explanation, various specific details are set forth in order toprovide a thorough understanding of preferred embodiments. It will beevident, however, to one skilled in the art that the present disclosuremay be practiced without these specific details.

A CPE device includes, but is not limited to, for example a controller,e.g. a microprocessor, a memory, in which an operating system is storedfor the operation of the CPE device, a wireless node for a wirelesscommunication, and a circuit for a broadband connection, e.g. an xDSLconnection. The wireless node includes, but is not limited to, asoftware driver, a physical layer with data buffers, and an antenna. ACPE device of this kind is for example an access gateway.

The wireless node is controlled by the software driver which executes anumber of background tasks during operation of the wireless node, e.g.dynamic rate adaptation, packet aggregation, channel quality monitoring,and the like. On top of signal manipulations, the wireless driver alsoembeds an IEEE 802.11 protocol stack with the associated IEEE definedmanagement and control messaging. The wireless driver will hence injecta number of management and control packets in the data stream, making itdifficult to analyze a link by transparently looking at the data frameexchange only.

An arrangement illustrating a wireless communication is schematicallydepicted in FIG. 1: An access point 1 communicates with a station 2, oruser equipment 2, via a wireless link 3. A station 2 may be for examplea smartphone, a tablet, a laptop, etc. The access point 1 includes acircuit comprising a microprocessor 10, a memory 11, a wireless node 12for the wireless link, and a monitor application 13. The station 2includes a second circuit comprising a microprocessor 20, a memory 21,and a wireless node 22 for the wireless link. The wireless node 12includes a physical layer 14 and a link layer 15, and the wireless node22 includes a physical layer 24 and a link layer 25. The access point 1is in particular a CPE device, for example a residential gatewayestablishing with the station 2 a home network of an end user. Themonitor application 13 is included for analyzing and evaluating thewireless link 3 and retrieves in particular parameters representative oftransmission conditions of the wireless link 3.

The monitor application 13 comprises instructions for the microprocessor10 and the monitor application 23 comprises instructions for themicroprocessor 20, which are included for diagnosing the wireless link 3and which gather an information set about the wireless link 3. Theinformation set includes in particular actual data rate, physical layerdata rate, number of spatial streams, channel bandwidth, mediumavailability and Received Signal Strength Indicator (RSSI). Monitor dataare gathered in a passive mode, in which a data transmission ismonitored between the access point 1 and the station 2 or vice versa.

FIG. 2 illustrates the possibilities which have to be considered whendiagnosing the Wi-Fi performance between the access point 1 and thestation 2. A unidirectional link 3′ from the access point 1 to thestation 2 is examined. The theoretical maximum data rate 30 for thislink is given by the capabilities of the access point 1 and the station2, called here MaxNegotiatedPhyRate or MaxPhyRate, which is for example130 MB/s in case an IEEE 802.11n standard with 20 MHz channel bandwidthand two spatial streams is selected for the transmission between theaccess point 1 and the station 2. This is thus the maximum achievablelink speed, 100%, which is only a theoretical value, because for mostsituations physical limitations come into play: the received signalstrength RSSI at the station side is reduced for example due to thedistance between the access point 1 and the station 2 and path loss dueto any walls or other obstacles and reflections. Also the number ofspatial streams has to be determined. The practically attainable datarate 31, called here PhysLimitsPhyRate, is therefore less than the datarate 30.

Further performance can be lost due to interference close to the station2, which is not seen by the access point 1, called here far endinterference FEIF: this can be any microwave source like RF Babyphone,microwave oven or a hidden Wi-Fi node, and leads to a further reduceddata rate, called here TrainedPhyRate 32. Similar interference canappear at the access point 1, called here near end interference NEIF:This will reduce the available data rate 32 to a data rate 33,MediumBusyOtherWiFi. Further performance can be lost by sharing themedium with other Wi-Fi traffic, which can be caused by WLAN traffic inthe home network, but also by Wi-Fi traffic of a neighboring network.

In order to monitor the data traffic of the physical layer, the layer 1of the OSI (Open Systems Interconnection Model) model, transmissionconditions of the traffic that is transmitted and received by the Wi-Finode of the residential gateway, the residential gateway includes amonitor application receiving all received and transmitted packets. Themonitor application has access to the following blocks:

Transmit (TX) packet queue, TX packets

Receive (RX) packet queue, RX packets

Transmit/Receive signal indicators (RSSI)

Although there are solutions describing a link between networktransmission conditions, such as packet loss, latency and bandwidth andQoE, none of them teaches a link between parameters representative ofthe transmission conditions of a wireless link, such as a Wi-Fi link,Phy Rate; Wi-Fi medium business; etc., and QoE s.

FIG. 3 represents a flow chart illustrating the steps of a method forpredicting a level of QoE of an application intended to be run on astation according to one or more embodiments of the invention. Anapplication is for example web-browsing, video streaming, voice over IP(VoIP), etc. The application is run on a station 2 or user equipment 2,such as a smartphone, or a computer connected to the Internet through aWi-Fi link. In the following example, the considered application isweb-browsing.

In order to determine the influence of Wi-Fi transmission conditions onthe QoE for a given application, a plurality of transmission conditionsof the Wi-Fi link are defined and for each of these transmissionconditions, parameters representative of the transmission conditions arecollected passively by the access point, during a leaning phase. Thesecollected parameters are then processed by the monitor application 13,23. In the meantime, i.e. during the learning phase, the station 2performs a series of web-browsing tasks under the different transmissionconditions of the Wi-Fi link and each time, a parameter representativeof the QoE is measured.

Then a mapping is done between the parameters representative of thetransmission conditions of the Wi-Fi link processed by the monitorapplication 13, 23 and the measured parameter representative of the QoE.

The learning phase comprises steps E1 to E6 of the method according toan embodiment of the invention.

This mapping is then used to predict a level of QoE of an applicationintended to be run on the station. In order to do so, the parametersrepresentative of the transmission conditions of the Wi-Fi link arecollected passively by the access point. Then using the mapping obtainedduring the learning phase, an expected level of QoE for the applicationis obtained.

Thus, during step E1 different transmission conditions of a Wi-Fi linkare determined. In order to do so, the wireless transmission conditionsare modified, for example, along axis: PhyRate and medium availability.

A variation in PhyRate is realised by introducing attenuation on thetransmission path established between the access point and the station.For example, attenuations of 6, 12, 15, 18, 19 and 20 dBs areintroduced, reducing the PhyRate.

The medium may be unavailable due to interferences from both Wi-Fi andno Wi-Fi communications. In order to emulate non Wi-Fi communications anarrowband sinewave signal is generated to block the access point CCA.In order to emulate Wi-Fi communications, a second pair accesspoint/station generates competing Wi-Fi traffic which blocks the medium.

The different transmission conditions of the Wi-Fi link correspond tothe different combinations of PhyRate and medium availability scenarios.

During a step E2, sets of parameters representative of the transmissionconditions of the Wi-Fi are collected passively by the access point foreach of the transmission conditions defined during step E1 by themonitor application 13, 23.

In the meantime, during a step E3, the station 2 performs a series ofweb-browsing tasks under the different transmission conditions of theWi-Fi link determined during step E1, and each time, a parameterrepresentative of the QoE is measured. In the case of web-browsing, sucha parameter representative of the QoE is for example the page load time.

Thus during step E3, the station 2 accesses 10 times web pages such asgoogle.com, twitter.com, amazon.fr, etc., and each time, the page loadtime is measured.

During a step E4, the page load time of a web page is mapped with a meanopinion score (MOS) defined in ITU-T recommendation G.1030. The MOS isan integer comprised between 1 and 5, 1 corresponding to the lowest QoEscore and 5 corresponding to the highest QoE score. Thus the longer thepage load time the lower the MOS. For example a MOS of 1 corresponds toa page load time of 10 seconds, since users usually lost focus after 10seconds of waiting; and a MOS of 5 corresponds to the mean page loadtime per web page.

During a step E5, a mapping between the sets of parametersrepresentative of the transmission conditions of the Wi-Fi link and theparameter representative of the level of QoE of the application measuredfor each web page access is computed.

For example a vector representing both the transmission conditions andthe MOS is generated for each web page access. The RRSI and averageTx/Rx PHY rate strongly correlate with variations in the SNR of theWi-Fi link since they indirectly measure the link quality. Informationrepresentative of the medium unavailability due to Wi-Fi and no Wi-Fitraffic is also reported. These vectors then feed a predictor such asthe Support Vector Regressor (SVR).

The mapping obtained during step E5 enables to estimate an expectedlevel of QoE considering only Wi-Fi effects.

In a step E6, a selection is performed to determine which parametersrepresentative of the transmission conditions of the Wi-Fi from the setof parameters collected by the access point are the most relevant forpredicting the level of QoE for a given application.

These selected parameters called parameters representative of thetransmission conditions of the wireless link of significant influence onthe level of QoE of the given application are obtained by feeding thepredictor with different combinations of parameters representative ofthe transmission conditions of the wireless link and checking theaccuracy of the predicted QoE with the MOS corresponding to the set ofparameters representative of the transmission conditions of the wirelesslink from which the combinations of parameters representative of thetransmission conditions of the wireless link are extracted.

For example, for web-browsing, the parameters representative of thetransmission conditions of the wireless link of significant influence onthe level of QoE are the average Tx PHY rate, the frame delivery ratio,MediumBusy, and MediumBusyOtherWi-Fi.

In a step E7, parameters representative of the transmission conditionsof the Wi-Fi link are collected passively by the access point on aperiodic basis.

In a step E8, a level of QoE is computed for a given application, e.g.web-browsing, by using the mapping obtained on step 5. The parametersused for computing the level of QoE are either the set of parametersrepresentative of the transmission conditions of the Wi-Fi linkcollected by the access point, or depending on the application intendedto be run on the station, parameters representative of the transmissionconditions of the wireless link of significant influence on the level ofQoE as selected during step E6.

Although the present invention has been described hereinabove withreference to specific embodiments, the present invention is not limitedto the specific embodiments, and modifications will be apparent to askilled person in the art which lie within the scope of the presentinvention.

Many further modifications and variations will suggest themselves tothose versed in the art upon making reference to the foregoingillustrative embodiments, which are given by way of example only andwhich are not intended to limit the scope of the invention, that beingdetermined solely by the appended claims. In particular the differentfeatures from different embodiments may be interchanged, whereappropriate.

The invention claimed is:
 1. A method for predicting Quality ofExperience (QoE) of an application running on a network device, themethod comprising: collecting a plurality of wireless parametersrepresentative of wireless transmission conditions, wherein eachwireless parameter of the plurality of wireless parameters is measuredduring a plurality of learning periods, wherein for each learning periodwireless transmission conditions are modified by varying PhyRate andmedium availability, wherein the plurality of wireless parameters isselected from the group comprising of physical layer data rate, numberof spatial streams, channel bandwidth, medium availability, and ReceivedSignal Strength Indicator, RSSI; collecting one or more QoE parametersrelated to an application, wherein each QoE parameter of the one or moreof QoE parameters is measured during the plurality of learning periods;mapping the plurality of wireless parameters to the one or more QoEparameters; and selecting, based on the mapping, one or morerepresentative wireless parameters from the plurality of wirelessparameters that most significantly influences the one or more QoEparameters in order to enable a device to predict a QoE level for theapplication for a wireless environment of a first time period by onlymeasuring the one or more representative wireless parameters, whereinthe selecting is performed by feeding a predictor with differentcombinations of the plurality of wireless parameters and checking anaccuracy of the predicted QoE level with a Mean Opinion Score (MOS) foreach combination of the different combinations.
 2. The method of claim1, wherein the one or more QoE parameters are measured at a networkdevice.
 3. The method of claim 2, wherein each of the plurality ofwireless parameters is measured at an access point (AP).
 4. The methodof claim 3, wherein a QoE parameter of the one or more QoE parameters isa mean opinion score (MOS) based on a page load time of the application.5. The method of claim 4, wherein the plurality of wireless parametersis selected from the group comprising of actual data rate, physicallayer data rate, number of spatial streams, channel bandwidth, andReceived Signal Strength Indicator (RSSI).
 6. The method of claim 5,wherein the plurality of wireless parameters are collected bymonitoring, at the AP, data transmissions between the AP and the networkdevice.
 7. The method of claim 6, wherein the application is a webbrowser.
 8. The method of claim 7, wherein the first time period doesnot overlap with the set of learning periods.
 9. The method of claim 8,wherein the AP is part of a customer premises equipment.
 10. The methodof claim 8, wherein the AP is a gateway.
 11. The method of claim 8,wherein the network device is a station (STA).
 12. An access point (AP)comprising: a processor; a wireless node operatively connected to theprocessor; wherein the processor and wireless node are configured to:collect a plurality of wireless parameters representative of wirelesstransmission conditions, wherein each wireless parameter of theplurality of wireless parameters is measured during a plurality oflearning periods, wherein for each learning period wireless transmissionconditions are modified by varying PhyRate and medium availability,wherein the plurality of wireless parameters is selected from the groupcomprising of physical layer data rate, number of spatial streams,channel bandwidth, medium availability, and Received Signal StrengthIndicator, RSSI; collect one or more QoE parameters related to anapplication, wherein each QoE parameter of the plurality of QoEparameters is measured during the plurality of learning periods; map theplurality of wireless parameters to the one or more QoE parameters; andselect, based on the mapping, one or more representative wirelessparameters from the plurality of wireless parameters that mostsignificantly influences the one or more QoE parameters in order toenable a device to predict a QoE level for the application for awireless environment of a first time period by only measuring the one ormore representative wireless parameters, wherein the selecting isperformed by feeding a predictor with different combinations of theplurality of wireless parameters and checking an accuracy of thepredicted QoE level with a Mean Opinion Score (MOS) for each combinationof the different combinations.
 13. The AP of claim 12, wherein the oneor more QoE parameters are measured at a network device, and wherein thenetwork device is a station.
 14. The AP of claim 13, wherein a QoEparameter of the one or more QoE parameters is a mean opinion score(MOS) based on a page load time of the application.
 15. The AP of claim14, wherein the plurality of wireless parameters is selected from thegroup comprising of actual data rate, physical layer data rate, numberof spatial streams, channel bandwidth, and Received Signal StrengthIndicator (RSSI).
 16. The AP of claim 15, wherein the plurality ofwireless parameters are collected by monitoring, at the AP, datatransmissions between the AP and the network device.
 17. The AP of claim16, wherein the application is a web browser.
 18. The AP of claim 17,wherein the first time period does not overlap with the set of learningperiods.
 19. The method of claim 1, wherein the medium availability isdetermined by performing a clear channel assessment as part of thecollecting the plurality of wireless parameters.
 20. The AP of claim 12,wherein the medium availability is determined by performing a clearchannel assessment as part of the collecting the plurality of wirelessparameters.